Group 26 - Assignment 5
Carl Felix Freiesleben - s203521
Judith Tierno Martí - s222869
Intro
The analysis was performed on the dataset: Right Heart Catheterization (RHC) Dataset, first analysed Connors (et. al) (1996)
It focuses on the effect RHC has on the patients
Used propensity score matching to create an artificial control group
Their study found that patients undergoing RHC experienced shorter survival times.
Attribute datta includes patient demographics, socioeconomic details, physiological parameters, disease-related information, and survival outcomes.
Materials and Methods
We performed our analysis using \(\color{red}{\text{Tidyverse}}\).
Before cleaning and augmentation:
5735 patients
62 attributes
After cleaning and augmentation:
5612 patients
53 attributes
Materials and Methods (exploratory dataviz)
Familiarize ourselves with the data by extracting different information about the attributes and made numerous plots
Created summaries of different attributes, to find what makes sense to analyse
# A tibble: 4 × 3
# Groups: swang1 [2]
swang1 death n
<dbl> <dbl> <int>
1 0 0 1291
2 0 1 2177
3 1 0 681
4 1 1 1463
We used histograms because they are easy to read and interperate, while also showing a lot of information
*insert 2 small images as examples
Table 1
rhc_aug |> mutate(sex = factor(sex),
swang1 = factor(swang1),
death = factor(x = death, levels = c(0,1), c("Alive","Dead"))) |>
table1(x = formula(~ sex + age + race + swang1 | death),
data = _)
|
Alive
(N=1972) |
Dead
(N=3640) |
Overall
(N=5612) |
| sex |
|
|
|
| Female |
906 (45.9%) |
1594 (43.8%) |
2500 (44.5%) |
| Male |
1066 (54.1%) |
2046 (56.2%) |
3112 (55.5%) |
| age |
|
|
|
| Mean (SD) |
56.6 (17.4) |
64.0 (15.7) |
61.4 (16.7) |
| Median [Min, Max] |
58.0 [18.0, 102] |
66.0 [18.0, 101] |
64.0 [18.0, 102] |
| race |
|
|
|
| black |
323 (16.4%) |
577 (15.9%) |
900 (16.0%) |
| other |
121 (6.1%) |
223 (6.1%) |
344 (6.1%) |
| white |
1528 (77.5%) |
2840 (78.0%) |
4368 (77.8%) |
| swang1 |
|
|
|
| 0 |
1291 (65.5%) |
2177 (59.8%) |
3468 (61.8%) |
| 1 |
681 (34.5%) |
1463 (40.2%) |
2144 (38.2%) |
Investigating the mean blood pressure with different diseases
- Bimodal distributions
- Seems that mean blood pressure is higher in patients without RHC
How Your Medical Insurance Influences Your Survival Chances
- Most patients have low income.
- Patients are mostly covered by Medicare or Private medical assurance.
- Individuals in lower income categories have a higher mortality.
- Individuals covered by Medicare have the highest mortality.
Modelling
More beautiful plots by Emilie
Discussion
How come we found no major discoveries?
What could have been done differently?
Conclusion
We can conclude that PC can make sense for further analysis.
We can conclude that high values of APS for several diagnosis, will increase the risk of death
